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23-01-cse5004

2023-01학기 파이썬을이용한과학계산 (CSE5004)

deepxde

A library for scientific machine learning and physics-informed learning

dinn

Disease Informed Neural Networks (DINNs) — neural networks capable of learning how diseases spread, forecasting their progression, and finding their unique parameters (e.g. death rate).

epidrlearn

EpidRLearn: Learning intervention strategies for epidemics with Reinforcement Learning

epirl

Reinforcement Learning for Epidemiology modeling

ezlab-rl

learning-python-physics-informed-machine-learning-pinns-deeponets

Physics Informed Machine Learning Tutorials (Pytorch and Jax)

optimal-control-biological

Optimal Control Theory's study based on Suzzane Lenhart's book. Theoretical notes and numeric simulation developed.

physics-informed-deeponets

pinn_masterthesis

A Study on Physics Informed Neural Networks, with Applications for Compartment Models

pinn_tf2

Implementation of PINNs in TensorFlow 2

pinns

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

pinns-1

PyTorch Implementation of Physics-informed Neural Networks

pinns-tf2.0

TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

rl-epidemic-benchmark

reference1 : RL-epidemic

stgnn-for-covid-in-korea

Spatio-temporal graph neural network for predicting COVID-19 new cases in Korea.